/study-design-tool-gate
Classify a paper's study design (RCT, cohort, case-control, diagnostic-accuracy, systematic-review, animal-study, prediction-model, etc., or not_applicable) and dispatch to the correct downstream bias-risk/quality/reporting tool and specific variant (CASP has 8 variants, JBI ~6,
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill study-design-tool-gate --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/study-design-tool-gate
Context preview
The summary Claude sees to decide when to auto-load this skill.
Classify a paper's study design (RCT, cohort, case-control, diagnostic-accuracy, systematic-review, animal-study, prediction-model, etc., or not_applicable) and dispatch to the correct downstream bias-risk/quality/reporting tool and specific variant (CASP has 8 variants, JBI ~6,
SKILL.md
study-design-tool-gate.SKILL.mdname: study-design-tool-gate
description: Classify a paper's study design (RCT, cohort, case-control, diagnostic-accuracy, systematic-review, animal-study, prediction-model, etc., or not_applicable) and dispatch to the correct downstream bias-risk/quality/reporting tool and specific variant (CASP has 8 variants, JBI ~6, RoB2 has parallel/cluster/crossover versions). Use this as the mandatory first step before running ANY of CASP, JBI, AMSTAR-2, NOS, RoB2, ROBINS-I, QUADAS-2, CONSORT, STROBE, ARRIVE, SPIRIT, TRIPOD, or engineering-config-grading — these tools are all study-design-conditional and picking the wrong variant produces meaningless results. It is entirely correct and common for this gate to determine that none of these medically-descended tools applies (e.g. most CS/ML papers) — that is a valid, complete answer, not a failure.
version: 1.0.0
category: paper-reading
type: sop
execution: subagent
prompt: ./prompt.md
input: 'source_path (string), meta_path (string)'
reads: 'abstract and method sections only'
output: 'study_design (string), dispatched_tool (string), applicability_reasoning (string)'
dependencies:
sops:
- spawn-agent
Study Design Tool Gate
Classifies study design and dispatches to the right bias-risk/quality/reporting tool + variant — or determines none applies. Added per coverage-audit M11: the original graph had no node representing this dispatch decision at all; every A1/A2 tool was drawn as if it started with no gate.
Execution
Subagent — spawned via spawn-agent skill.
Reference
`references/tool-dispatch-table.md` — the full dispatch table (every study_design → tool + variant mapping). Read before drafting the prompt's decision, not summarized inline here (kept out of this SKILL.md body per Progressive Disclosure).
"not_applicable" Is a Correct, Common Answer
This is worth restating: most of these tools carry medical/clinical assumptions baked into their domains, and forcing a dispatch onto a paper that has no matching study design produces a meaningless result, not a conservative one. Do not treat a high not_applicable rate across a batch of CS/ML papers as a sign this SOP is failing to trigger correctly.
<!-- BEGIN available-tables (generated) -->
Available SOPs
| SOP | When to use | | --- | --- | | spawn-agent | Spawn a customized CC subagent with full MCP tool access. |
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Read more
name: study-design-tool-gate description: Classify a paper's study design (RCT, cohort, case-control, diagnostic-accuracy, systematic-review, animal-study, prediction-model, etc., or not_applicable) and dispatch to the correct downstream bias-risk/quality/reporting tool and specific variant (CASP has 8 variants, JBI ~6, RoB2 has parallel/cluster/crossover versions). Use this as the mandatory first step before running ANY of CASP, JBI, AMSTAR-2, NOS, RoB2, ROBINS-I, QUADAS-2, CONSORT, STROBE, ARRIVE, SPIRIT, TRIPOD, or engineering-config-grading — these tools are all study-design-conditional and picking the wrong variant produces meaningless results. It is entirely correct and common for this gate to determine that none of these medically-descended tools applies (e.g. most CS/ML papers) — that is a valid, complete answer, not a failure. version: 1.0.0 category: paper-reading type: sop execution: subagent prompt: ./prompt.md input: 'source_path (string), meta_path (string)' reads: 'abstract and method sections only' output: 'study_design (string), dispatched_tool (string), applicability_reasoning (string)' dependencies: sops: - spawn-agent
Study Design Tool Gate
Classifies study design and dispatches to the right bias-risk/quality/reporting tool + variant — or determines none applies. Added per coverage-audit M11: the original graph had no node representing this dispatch decision at all; every A1/A2 tool was drawn as if it started with no gate.
Execution
Subagent — spawned via spawn-agent skill.
Reference
`references/tool-dispatch-table.md` — the full dispatch table (every study_design → tool + variant mapping). Read before drafting the prompt's decision, not summarized inline here (kept out of this SKILL.md body per Progressive Disclosure).
"not_applicable" Is a Correct, Common Answer
This is worth restating: most of these tools carry medical/clinical assumptions baked into their domains, and forcing a dispatch onto a paper that has no matching study design produces a meaningless result, not a conservative one. Do not treat a high not_applicable rate across a batch of CS/ML papers as a sign this SOP is failing to trigger correctly.
<!-- BEGIN available-tables (generated) -->
Available SOPs
| SOP | When to use | | --- | --- | | spawn-agent | Spawn a customized CC subagent with full MCP tool access. |
<!-- END available-tables (generated) -->
The complete research orchestration system for AI-native science. What It Does Design Philosophy Architecture (v3.2.2) Quick Start Configuration Roadmap License DARE is not a tool that helps you do research. It is the researcher.
Repo: yogsoth-ai/de-anthropocentric-research-engine
Other skills on de-anthropocentric-research-engine.
- /formated-results
Closing skill for the research-executor, loaded as the last step of formated-specs. Summarize the design just produced into one research-result JSON fenced block in your reply. Do not execute the research.
Open skill - /formated-specs
Spec-slot skill for the research-executor. Emit the 4-layer DARE orchestration of the assigned topic as one research-graph JSON fenced block in your reply. Replaces the generic spec-writing step.
Open skill - /injection-fidelity
Loss-1 judge (codex role). Given one sample's de-identified dialogue and its PolicyCard, decide axis-by-axis whether the user-simulator enacted the card's per-axis pressure. Judge enactment of the card, never whether the research is good.
Open skill - /ladder-quality-order
Loss-2 judge (codex role). Over one topic's 6 shuffled research-design samples, pairwise-rank by quality using the D1–D5 standard. Emit the pairwise log; the harness computes the order and the ladder verdicts. Judge quality difference, never against academic standards.
Open skill - /optimization-loop
The optimizer brain for the ladder-foundry pretraining loop. Runs the two-level nested batch loop, delegates gating to gate_eval, attributes a failing batch to one weight (attribute-first), and recovers from disk after compaction. Control flow is fully scripted; only the
Open skill - /acu-nugget-recall
Tactic: Extract atomic units from one paper and score how much of a caller-supplied summary covers. Use for ACU-style binary or Nugget-style ternary recall checks; cannot run without a target summary.
Open skill

